OpenBB/openbb_platform/extensions/quantitative

OpenBB Quantitative Extension

This package adds the openbb-quantitative extension to the Open Data Platform by OpenBB.

It provides a quantitative analysis toolkit — normality and unit root tests, CAPM risk measures, descriptive statistics, rolling-window statistics, risk-adjusted performance ratios, and multi-factor regression / attribution / risk-decomposition — that operate on any tabular dataset passed in as data.

Installation

Install from PyPI with:

pip install openbb-quantitative

Then build the Python static assets by running:

openbb-build

Quick Start

Every command is a POST endpoint that takes a data payload (a list of records, e.g. the .results of another OpenBB command) plus typed parameters, and returns an OBBject.

from openbb import obb

prices = obb.equity.price.historical(
    symbol="AAPL", start_date="2023-01-01", provider="yfinance"
).results

# Descriptive summary statistics of a series.
obb.quantitative.summary(data=prices, target="close")

# Augmented Dickey-Fuller and KPSS unit root tests.
obb.quantitative.unitroot_test(data=prices, target="close")

# Rolling standard deviation over a moving window.
obb.quantitative.rolling.stdev(data=prices, target="close", window=21)

# Rolling Sharpe ratio.
obb.quantitative.performance.sharpe_ratio(data=prices, target="close")

Working with factors

The factor endpoints take two payloads — a target series and a factor matrix — plus an optional risk-free column name. Pair with any factor source; for Fama-French data the openbb-famafrench provider exposes the canonical research datasets.

target = obb.equity.price.historical("SPY", provider="yfinance").results
factors = obb.famafrench.factors(provider="famafrench").results

# Multi-period regression: betas, p-values, CIs, R-squared per named window.
obb.quantitative.factors(
    data=target, factors_data=factors, target="close", risk_free_column="rf"
)

# Share of Var(target) attributable to each factor (residual sums to 1 - R^2).
obb.quantitative.risk_decomposition(
    data=target, factors_data=factors, target="close", risk_free_column="rf"
)

# Decompose the period's total return into factor contributions + alpha + residual.
obb.quantitative.attribution(
    data=target, factors_data=factors, target="close", risk_free_column="rf"
)

# Refit OLS on a sliding window to track time-varying factor exposures.
obb.quantitative.rolling.factors(
    data=target, factors_data=factors, target="close",
    window=252, step=21, risk_free_column="rf",
)

To use the extension over HTTP, start the API server with openbb-api and POST to /api/v1/quantitative/<command>.

Coverage

All commands are available under obb.quantitative.*.

Metrics

• normality — kurtosis, skewness, Jarque-Bera, Shapiro-Wilk, and Kolmogorov-Smirnov normality tests
• capm — Capital Asset Pricing Model risk measures
• unitroot_test — Augmented Dickey-Fuller and KPSS unit root tests
• summary — descriptive summary statistics of a series

Factor analysis

• factors — multi-period OLS regression of a target series on a factor matrix; returns coefficient, p-value, 95% CI, and R-squared per (period, factor)
• risk_decomposition — share of Var(target) attributable to each factor plus residual; per-period factor shares sum to R-squared and the residual share to 1 - R-squared
• attribution — additive decomposition of the period's total target return into factor contributions, alpha, and residual

Rolling

• rolling.skew — rolling skew over a moving window
• rolling.variance — rolling variance over a moving window
• rolling.stdev — rolling standard deviation over a moving window
• rolling.kurtosis — rolling kurtosis over a moving window
• rolling.mean — rolling mean over a moving window
• rolling.quantile — rolling quantile over a moving window
• rolling.factors — rolling-window factor regression; emits per-factor betas and t-statistics at each window end

Stats

• stats.skew — skewness of a series
• stats.variance — variance of a series
• stats.stdev — standard deviation of a series
• stats.kurtosis — kurtosis of a series
• stats.mean — arithmetic mean of a series
• stats.quantile — quantile of a series

Performance

• performance.omega_ratio — Omega ratio across a range of return thresholds
• performance.sharpe_ratio — rolling Sharpe ratio
• performance.sortino_ratio — rolling Sortino ratio

Charts

The extension also ships chart views, auto-discovered by openbb-charting:

• factors — coefficient heatmap colored by p-value
• risk_decomposition — stacked horizontal bars of variance shares per period
• attribution — stacked horizontal bars of return contributions per period (signs preserved)
• rolling.factors — stacked area chart of rolling factor exposure over time.

See the full docs here